Overview

Dataset statistics

Number of variables7
Number of observations8525
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory532.8 KiB
Average record size in memory64.0 B

Variable types

DateTime1
TimeSeries5
Numeric1

Timeseries statistics

Number of series5
Time series length8525
Starting point1986-03-13 00:00:00
Ending point2020-01-07 00:00:00
Period1 day, 10 hours and 46 minutes
2023-11-17T10:26:00.721176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:26:01.397942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Alerts

Open is highly overall correlated with High and 3 other fieldsHigh correlation
High is highly overall correlated with Open and 3 other fieldsHigh correlation
Low is highly overall correlated with Open and 3 other fieldsHigh correlation
Close is highly overall correlated with Open and 3 other fieldsHigh correlation
Adj Close is highly overall correlated with Open and 3 other fieldsHigh correlation
Open is non stationaryNon stationary
High is non stationaryNon stationary
Low is non stationaryNon stationary
Close is non stationaryNon stationary
Adj Close is non stationaryNon stationary
Open is seasonalSeasonal
High is seasonalSeasonal
Low is seasonalSeasonal
Close is seasonalSeasonal
Adj Close is seasonalSeasonal
Date has unique valuesUnique

Reproduction

Analysis started2023-11-17 15:25:45.186515
Analysis finished2023-11-17 15:26:00.366928
Duration15.18 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Date
Date

UNIQUE 

Distinct8525
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size133.2 KiB
Minimum1986-03-13 00:00:00
Maximum2020-01-07 00:00:00
2023-11-17T10:26:01.894616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:26:02.187392image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Open
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct4668
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.220247
Minimum0.088542
Maximum159.45
Zeros0
Zeros (%)0.0%
Memory size133.2 KiB
2023-11-17T10:26:02.608761image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.088542
5-th percentile0.355903
Q13.414063
median26.174999
Q334.23
95-th percentile95.827998
Maximum159.45
Range159.36146
Interquartile range (IQR)30.815937

Descriptive statistics

Standard deviation28.626752
Coefficient of variation (CV)1.0144047
Kurtosis4.3788963
Mean28.220247
Median Absolute Deviation (MAD)16.542186
Skewness1.891849
Sum240577.6
Variance819.49093
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1
2023-11-17T10:26:03.018288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-11-17T10:26:03.995231image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps1765
min3 days
max1 week
mean3 days, 2 hours and 57 minutes
std8 hours, 13 minutes and 22.87 seconds
2023-11-17T10:26:08.861488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2.65625 18
 
0.2%
27.01 17
 
0.2%
0.361111 16
 
0.2%
0.380208 15
 
0.2%
0.409722 13
 
0.2%
0.355903 12
 
0.1%
0.364583 12
 
0.1%
0.375 12
 
0.1%
0.397569 12
 
0.1%
2.515625 12
 
0.1%
Other values (4658) 8386
98.4%
ValueCountFrequency (%)
0.088542 1
 
< 0.1%
0.090278 1
 
< 0.1%
0.092014 1
 
< 0.1%
0.092882 1
 
< 0.1%
0.094618 5
0.1%
0.095486 6
0.1%
0.09592 1
 
< 0.1%
0.096354 5
0.1%
0.097222 6
0.1%
0.09809 8
0.1%
ValueCountFrequency (%)
159.449997 1
< 0.1%
159.320007 1
< 0.1%
158.990005 1
< 0.1%
158.779999 1
< 0.1%
158.320007 1
< 0.1%
158.119995 1
< 0.1%
157.559998 1
< 0.1%
157.479996 1
< 0.1%
157.350006 1
< 0.1%
157.080002 1
< 0.1%
2023-11-17T10:26:03.360525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

High
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct4626
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.514473
Minimum0.092014
Maximum160.73
Zeros0
Zeros (%)0.0%
Memory size133.2 KiB
2023-11-17T10:26:12.583738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.092014
5-th percentile0.361111
Q13.460938
median26.5
Q334.669998
95-th percentile96.526001
Maximum160.73
Range160.63798
Interquartile range (IQR)31.20906

Descriptive statistics

Standard deviation28.848988
Coefficient of variation (CV)1.0117314
Kurtosis4.332717
Mean28.514473
Median Absolute Deviation (MAD)16.679687
Skewness1.8815801
Sum243085.89
Variance832.26409
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1
2023-11-17T10:26:12.992142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-11-17T10:26:13.871027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps1765
min3 days
max1 week
mean3 days, 2 hours and 57 minutes
std8 hours, 13 minutes and 22.87 seconds
2023-11-17T10:26:16.887433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
27.5 17
 
0.2%
0.369792 15
 
0.2%
0.364583 15
 
0.2%
26 15
 
0.2%
0.381944 15
 
0.2%
0.362847 14
 
0.2%
0.392361 14
 
0.2%
0.395833 14
 
0.2%
30 13
 
0.2%
2.820313 13
 
0.2%
Other values (4616) 8380
98.3%
ValueCountFrequency (%)
0.092014 1
 
< 0.1%
0.092882 1
 
< 0.1%
0.095486 3
 
< 0.1%
0.096354 2
 
< 0.1%
0.097222 6
0.1%
0.09809 7
0.1%
0.098958 8
0.1%
0.099826 4
< 0.1%
0.100694 8
0.1%
0.101563 8
0.1%
ValueCountFrequency (%)
160.729996 1
< 0.1%
159.949997 1
< 0.1%
159.669998 1
< 0.1%
159.550003 1
< 0.1%
159.100006 1
< 0.1%
159.020004 1
< 0.1%
158.729996 1
< 0.1%
158.490005 1
< 0.1%
158.119995 1
< 0.1%
157.770004 1
< 0.1%
2023-11-17T10:26:13.305256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

Low
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct4636
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.918967
Minimum0.088542
Maximum158.33
Zeros0
Zeros (%)0.0%
Memory size133.2 KiB
2023-11-17T10:26:20.653602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.088542
5-th percentile0.3475692
Q13.382813
median25.889999
Q333.75
95-th percentile94.169998
Maximum158.33
Range158.24146
Interquartile range (IQR)30.367187

Descriptive statistics

Standard deviation28.370344
Coefficient of variation (CV)1.0161674
Kurtosis4.4133439
Mean27.918967
Median Absolute Deviation (MAD)16.297501
Skewness1.8986703
Sum238009.19
Variance804.87642
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1
2023-11-17T10:26:21.081076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-11-17T10:26:22.017437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps1765
min3 days
max1 week
mean3 days, 2 hours and 57 minutes
std8 hours, 13 minutes and 22.87 seconds
2023-11-17T10:26:25.587245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
25.5 18
 
0.2%
27 17
 
0.2%
2.65625 17
 
0.2%
0.378472 17
 
0.2%
0.359375 16
 
0.2%
0.347222 14
 
0.2%
0.355903 14
 
0.2%
2.578125 14
 
0.2%
28.799999 14
 
0.2%
0.373264 13
 
0.2%
Other values (4626) 8371
98.2%
ValueCountFrequency (%)
0.088542 1
 
< 0.1%
0.08941 2
 
< 0.1%
0.090278 1
 
< 0.1%
0.091146 3
 
< 0.1%
0.092882 1
 
< 0.1%
0.09375 1
 
< 0.1%
0.094618 10
0.1%
0.095052 1
 
< 0.1%
0.095486 6
0.1%
0.096354 6
0.1%
ValueCountFrequency (%)
158.330002 1
< 0.1%
158.220001 1
< 0.1%
158.059998 1
< 0.1%
157.399994 1
< 0.1%
157.330002 1
< 0.1%
157.270004 1
< 0.1%
157.119995 1
< 0.1%
156.729996 1
< 0.1%
156.509995 1
< 0.1%
156.449997 1
< 0.1%
2023-11-17T10:26:21.442059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

Close
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct4823
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.22448
Minimum0.090278
Maximum160.61999
Zeros0
Zeros (%)0.0%
Memory size133.2 KiB
2023-11-17T10:26:29.401966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.090278
5-th percentile0.355903
Q13.414063
median26.16
Q334.23
95-th percentile95.405998
Maximum160.61999
Range160.52972
Interquartile range (IQR)30.815937

Descriptive statistics

Standard deviation28.626571
Coefficient of variation (CV)1.0142462
Kurtosis4.3776494
Mean28.22448
Median Absolute Deviation (MAD)16.549999
Skewness1.8913229
Sum240613.7
Variance819.48057
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1
2023-11-17T10:26:29.927622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-11-17T10:26:31.494474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps1765
min3 days
max1 week
mean3 days, 2 hours and 57 minutes
std8 hours, 13 minutes and 22.87 seconds
2023-11-17T10:26:34.983416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.361111 16
 
0.2%
27.25 13
 
0.2%
0.345486 12
 
0.1%
2.460938 12
 
0.1%
0.359375 12
 
0.1%
0.364583 11
 
0.1%
0.369792 11
 
0.1%
0.355903 11
 
0.1%
25.719999 11
 
0.1%
0.387153 11
 
0.1%
Other values (4813) 8405
98.6%
ValueCountFrequency (%)
0.090278 1
 
< 0.1%
0.092014 1
 
< 0.1%
0.092882 1
 
< 0.1%
0.09375 1
 
< 0.1%
0.094618 4
< 0.1%
0.095486 5
0.1%
0.09592 1
 
< 0.1%
0.096354 6
0.1%
0.097222 8
0.1%
0.09809 8
0.1%
ValueCountFrequency (%)
160.619995 1
< 0.1%
159.029999 1
< 0.1%
158.960007 1
< 0.1%
158.669998 1
< 0.1%
158.619995 1
< 0.1%
157.699997 1
< 0.1%
157.589996 1
< 0.1%
157.580002 1
< 0.1%
157.410004 2
< 0.1%
157.380005 1
< 0.1%
2023-11-17T10:26:30.292184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

Adj Close
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct6428
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.417934
Minimum0.058081
Maximum160.61999
Zeros0
Zeros (%)0.0%
Memory size133.2 KiB
2023-11-17T10:26:37.973199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.058081
5-th percentile0.228973
Q12.196463
median18.441576
Q325.392508
95-th percentile92.850152
Maximum160.61999
Range160.56191
Interquartile range (IQR)23.196045

Descriptive statistics

Standard deviation28.19533
Coefficient of variation (CV)1.2040059
Kurtosis5.921574
Mean23.417934
Median Absolute Deviation (MAD)13.129929
Skewness2.3298815
Sum199637.89
Variance794.97664
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1
2023-11-17T10:26:38.320567image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-11-17T10:26:39.178181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps1765
min3 days
max1 week
mean3 days, 2 hours and 57 minutes
std8 hours, 13 minutes and 22.87 seconds
2023-11-17T10:26:42.794179image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.232324 16
 
0.2%
0.222271 12
 
0.1%
1.583263 12
 
0.1%
0.231207 12
 
0.1%
0.239025 11
 
0.1%
0.249078 11
 
0.1%
0.24461 11
 
0.1%
0.228973 11
 
0.1%
0.237908 11
 
0.1%
0.236791 11
 
0.1%
Other values (6418) 8407
98.6%
ValueCountFrequency (%)
0.058081 1
 
< 0.1%
0.059198 1
 
< 0.1%
0.059756 1
 
< 0.1%
0.060315 1
 
< 0.1%
0.060873 4
< 0.1%
0.061432 5
0.1%
0.061711 1
 
< 0.1%
0.06199 6
0.1%
0.062549 8
0.1%
0.063107 8
0.1%
ValueCountFrequency (%)
160.619995 1
< 0.1%
159.029999 1
< 0.1%
158.960007 1
< 0.1%
158.669998 1
< 0.1%
158.619995 1
< 0.1%
157.699997 1
< 0.1%
157.589996 1
< 0.1%
157.580002 1
< 0.1%
157.410004 2
< 0.1%
157.380005 1
< 0.1%
2023-11-17T10:26:38.627053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

Volume
Real number (ℝ)

Distinct8337
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60456921
Minimum2304000
Maximum1.0317888 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size133.2 KiB
2023-11-17T10:26:45.719497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2304000
5-th percentile19646200
Q136679600
median53702400
Q374123500
95-th percentile1.2419282 × 108
Maximum1.0317888 × 109
Range1.0294848 × 109
Interquartile range (IQR)37443900

Descriptive statistics

Standard deviation38912246
Coefficient of variation (CV)0.64363593
Kurtosis81.284995
Mean60456921
Median Absolute Deviation (MAD)18336000
Skewness5.1468521
Sum5.1539525 × 1011
Variance1.5141629 × 1015
MonotonicityNot monotonic
2023-11-17T10:26:45.978743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18316800 4
 
< 0.1%
3427200 3
 
< 0.1%
82006400 3
 
< 0.1%
37843200 3
 
< 0.1%
48729600 3
 
< 0.1%
47577600 3
 
< 0.1%
58752000 3
 
< 0.1%
67750400 3
 
< 0.1%
69436800 3
 
< 0.1%
84297600 3
 
< 0.1%
Other values (8327) 8494
99.6%
ValueCountFrequency (%)
2304000 1
 
< 0.1%
2332800 1
 
< 0.1%
2419200 1
 
< 0.1%
2505600 1
 
< 0.1%
2822400 1
 
< 0.1%
3254400 1
 
< 0.1%
3427200 3
< 0.1%
3513600 1
 
< 0.1%
3542400 1
 
< 0.1%
3657600 1
 
< 0.1%
ValueCountFrequency (%)
1031788800 1
< 0.1%
788688000 1
< 0.1%
764504000 1
< 0.1%
591052200 1
< 0.1%
326028800 1
< 0.1%
324000000 1
< 0.1%
319317900 1
< 0.1%
317894400 1
< 0.1%
317750400 1
< 0.1%
313645800 1
< 0.1%

Interactions

2023-11-17T10:25:58.059328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:51.611423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:52.850106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:54.243328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:55.547557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:56.841990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:58.240459image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:51.829976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:53.088050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:54.469654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:55.784033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:57.043833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:58.426022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:52.035341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:53.309926image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:54.743942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:56.007978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:57.250541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:58.642961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:52.238910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:53.519224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:54.938575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:56.226051image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:57.462585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:58.843158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:52.440946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:53.721812image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:55.114876image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:56.428658image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:57.683569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:59.070374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:52.651524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:53.987571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:55.323774image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:56.653506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-17T10:25:57.892477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-17T10:26:46.156429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
OpenHighLowCloseAdj CloseVolume
Open1.0001.0001.0000.9990.982-0.353
High1.0001.0000.9991.0000.981-0.348
Low1.0000.9991.0001.0000.983-0.358
Close0.9991.0001.0001.0000.982-0.353
Adj Close0.9820.9810.9830.9821.000-0.375
Volume-0.353-0.348-0.358-0.353-0.3751.000

Missing values

2023-11-17T10:25:59.439113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-17T10:26:00.259177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateOpenHighLowCloseAdj CloseVolume
1986-03-131986-03-130.0885420.1015630.0885420.0972220.0625491031788800
1986-03-141986-03-140.0972220.1024310.0972220.1006940.064783308160000
1986-03-171986-03-170.1006940.1032990.1006940.1024310.065899133171200
1986-03-181986-03-180.1024310.1032990.0989580.0998260.06422467766400
1986-03-191986-03-190.0998260.1006940.0972220.0980900.06310747894400
1986-03-201986-03-200.0980900.0980900.0946180.0954860.06143258435200
1986-03-211986-03-210.0954860.0972220.0911460.0928820.05975659990400
1986-03-241986-03-240.0928820.0928820.0894100.0902780.05808165289600
1986-03-251986-03-250.0902780.0920140.0894100.0920140.05919832083200
1986-03-261986-03-260.0920140.0954860.0911460.0946180.06087322752000
DateOpenHighLowCloseAdj CloseVolume
2019-12-232019-12-23158.119995158.119995157.270004157.410004157.41000417718200
2019-12-242019-12-24157.479996157.710007157.119995157.380005157.3800058989200
2019-12-262019-12-26157.559998158.729996157.399994158.669998158.66999814520600
2019-12-272019-12-27159.449997159.550003158.220001158.960007158.96000718412800
2019-12-302019-12-30158.990005159.020004156.729996157.589996157.58999616348400
2019-12-312019-12-31156.770004157.770004156.449997157.699997157.69999718369400
2020-01-022020-01-02158.779999160.729996158.330002160.619995160.61999522622100
2020-01-032020-01-03158.320007159.949997158.059998158.619995158.61999521116200
2020-01-062020-01-06157.080002159.100006156.509995159.029999159.02999920813700
2020-01-072020-01-07159.320007159.669998157.330002157.580002157.58000218017762